


"""simple docstring"""


def 	snake_case		(  UpperCamelCase__							:    str      ,					UpperCamelCase__							:    str     ) ->   bool:
      lowerCamelCase       :       Dict   =						len(_lowerCamelCase     ) + 1
      lowerCamelCase       :       List[str]   =						len(_lowerCamelCase     ) + 1

      # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
      # length i of input_string matches with prefix string of length j of
      # given pattern.
      # "dp" stands for dynamic programming.
      lowerCamelCase       :       Tuple   =						[[0 for i in range(_lowerCamelCase     )] for j in range(_lowerCamelCase     )]

      # since string of zero length match pattern of zero length
      lowerCamelCase       :       Union[str, Any]   =						1

      # since pattern of zero length will never match with string of non-zero length
      for i in range(1      ,					_lowerCamelCase     ):
            lowerCamelCase       :       str   =						0

      # since string of zero length will match with pattern where there
      # is at least one * alternatively
      for j in range(1      ,					_lowerCamelCase     ):
            lowerCamelCase       :       Tuple   =						dp[0][j - 2] if pattern[j - 1] == """*""" else 0

      # now using bottom-up approach to find for all remaining lengths
      for i in range(1      ,					_lowerCamelCase     ):
            for j in range(1      ,					_lowerCamelCase     ):
                  if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                        lowerCamelCase       :       Optional[Any]   =						dp[i - 1][j - 1]

                  elif pattern[j - 1] == "*":
                        if dp[i][j - 2] == 1:
                              lowerCamelCase       :       int   =						1
                        elif pattern[j - 2] in (input_string[i - 1], "."):
                              lowerCamelCase       :       Any   =						dp[i - 1][j]
                        else:
                              lowerCamelCase       :       List[str]   =						0
                  else:
                        lowerCamelCase       :       str   =						0

      return bool(dp[-1][-1]     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # inputing the strings
    # input_string = input("input a string :")
    # pattern = input("input a pattern :")

    __lowerCamelCase   :Union[str, Any]						     =		'''aab'''
    __lowerCamelCase   :Union[str, Any]						     =		'''c*a*b'''

    # using function to check whether given string matches the given pattern
    if match_pattern(input_string, pattern):
        print(f"""{input_string} matches the given pattern {pattern}""")
    else:
        print(f"""{input_string} does not match with the given pattern {pattern}""")












"""simple docstring"""





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					DistilBertTokenizerFast
    SCREAMING_SNAKE_CASE		:	int		=					True






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      lowerCamelCase_       	=		DistilBertTokenizer.from_pretrained('distilbert-base-uncased'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]




